Model Assessment and Hyperparameter Tuning

This lecture covers the concepts of model assessment and hyperparameter tuning in machine learning. It explains the process of splitting data, estimating parameters, and finding the best hyperparameters using techniques like the validation set approach and cross-validation. The instructor demonstrates how to estimate test errors, select optimal degrees in polynomial regression, and apply resampling strategies. The lecture emphasizes the importance of tuning models to improve performance and avoid underfitting or overfitting. Practical examples and applications, such as using the bootstrap method for uncertainty estimation, are discussed.